Systems biology of mammalian cell division
Bibliographic record
Abstract
High-throughput screening technologies allow the identification of genes and proteins essential for mammalian cell division. However, the underlying complexity and connectivity of different biological processes, such as signal transduction, transcription, translation and proteolysis make it difficult to understand the mammalian cell cycle based on the analysis of its individual components alone. The recent development of robust and precise assays to study the mammalian cell cycle, in combination with functional genomics and proteomics, together provide the necessary tools to address this critical issue. With the implementation of different "Omics" technologies for quantitative and high-throughput data acquisition, the possibility of obtaining a more detailed view of the mammalian cell cycle is now realistic. Here, we review RNAi reagents, assays and validation strategies for the identification of genes functioning in the human cell cycle, and outline genomic, proteomic and microscopic approaches to further characterize their specific functions. While a fully integrated model of mammalian cell division remains a distant goal, a framework of a systems understanding of this medically relevant process is beginning to emerge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".